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| Section | Weight | Objectives |
|---|---|---|
| Data Quality Frameworks and Standards | 20% | - Other Industry Standards - ISO 8000 (Data Quality) - DAMA DMBOK Framework |
| Data Quality Assessment and Measurement | 25% | - Metrics and KPIs - Profiling Techniques - Root Cause Analysis - Benchmarking |
| Data Quality Governance | 15% | - Continuous Monitoring - Policies and Procedures - Data Stewardship - Data Quality Roles and Responsibilities |
| Data Quality Fundamentals | 20% | - Data Quality in the Data Lifecycle - Definition of Data Quality - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) |
| Data Quality Improvement | 20% | - Data Cleansing Techniques - Standardization - Validation Rules - Data Enrichment |
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NEW QUESTION # 61
Who does the DMBoK consider to be generally responsible for developing business glossary content?
Answer: A
Explanation:
DAMA-DMBOK2 assigns primary responsibility for business glossary content to Business Data Stewards.
Business Data Stewards are typically subject-matter experts who understand how data is defined, created, interpreted, and consumed within their business domain. DMBOK2 explicitly states that Data Stewards are generally responsible for business glossary content and identifies Business Data Stewards as professionals who work with stakeholders to define and control data.
A business glossary is not simply a technical dictionary. It establishes agreed business terminology, definitions, synonyms, business rules, responsible stewards, and relationships between business concepts.
This makes stewardship involvement essential because definitions must reflect operational and business meaning rather than merely database structures.
Data Architects may contribute candidate definitions and structural context from subject-area and conceptual models, but they do not generally own the business meaning. Business users provide valuable input, while Coordinating Data Stewards help reconcile definitions across domains, yet the normal accountability remains with Business Data Stewards.
This relationship is particularly important for Data Quality. Quality rules depend on unambiguous definitions of data elements. If "Customer," "Active Account," or "Order Date" has inconsistent meanings, measurements of completeness, accuracy, and validity cannot be consistently interpreted.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Develop a Business Glossary; Business Data Stewardship; Metadata Management; Chapter 13 - Data Quality Rules and Business Definitions.
NEW QUESTION # 62
Following the rollout of a data issue process, there have been no issues recorded in the first month. The reason for this might be:
Answer: B
Explanation:
A complete absence of reported issues immediately after introducing an enterprise Data Issue process is more likely to indicate lack of confidence in the governance process than genuinely flawless enterprise data. The certification material explicitly identifies lack of credibility in the process's ability to effect change as the plausible explanation.
Effective issue management depends on organizational trust. Employees need to believe that documenting an issue will lead to triage, ownership, escalation, root-cause investigation, remediation, and appropriate communication. If previous problems disappeared into a queue without action-or if raising defects creates organizational friction-users may simply stop reporting them.
DAMA-DMBOK2 treats Data Governance implementation as an organizational-change challenge rather than a purely procedural exercise. Governance must demonstrate authority, responsiveness, transparency, and measurable outcomes to establish credibility.
For Data Quality, issue volumes must also be interpreted carefully. "Zero issues reported" is not equivalent to
"zero defects." Complementary evidence should come from profiling, automated monitoring, quality metrics, user feedback, reconciliation, and operational outcomes.
Management should investigate reporting barriers, communicate resolved cases, establish clear escalation paths, and demonstrate that identified problems produce tangible improvements.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Governance Adoption and Credibility; Data Issue Management; Chapter 13 - Issue Identification, Escalation, Root-Cause Analysis and Remediation.
NEW QUESTION # 63
Database monitoring tools measure key database metrics, such as:
Answer: E
Explanation:
DAMA-DMBOK2 identifies capacity, availability, cache performance, and user statistics as representative metrics captured by database-monitoring tools. Database monitoring is an operational control mechanism used by Database Administrators and platform teams to understand whether database infrastructure is available, adequately sized, responsive, and being used as expected. DMBOK2 explicitly describes monitoring tools as automating the observation of metrics such as capacity, availability, cache performance, and user statistics.
Capacity metrics indicate resource consumption and future growth requirements. Availability measures whether data services remain accessible when required. Cache-performance measures help identify inefficient access patterns and bottlenecks, while user statistics provide information about workload and database consumption.
The other choices mix design concepts, CRUD operations, or Data Quality concepts with operational monitoring measures. For example, normalization is a modelling technique rather than a routine runtime performance metric. Similarly, "create, read, update, delete" describes basic data operations rather than monitoring indicators.
Although database performance and Data Quality are distinct disciplines, poor operational performance can affect Data Quality dimensions such as timeliness and availability. Monitoring therefore supports the technical environment in which governed, reliable information is delivered to users and applications.
Reference Topics: DAMA-DMBOK2 - Data Storage and Operations; Database Operations; Capacity and Availability Management; Monitoring; Chapter 13 - Timeliness and Operational Fitness for Purpose.
NEW QUESTION # 64
A company converts "Street", "St.", and "Str" to a single approved address representation before customer matching. This is an example of:
Answer: C
Explanation:
The activity is Standardization. Standardization converts semantically equivalent values into a consistent representation so they can be compared, matched, integrated, and interpreted reliably.
In the scenario, Street, St., and Str may represent the same address concept but differ syntactically.
Converting them to an approved standard reduces superficial variation before entity matching.
Standardization is commonly used for names, addresses, telephone numbers, units of measure, dates, product descriptions, geographic references, abbreviations, and coded values. It frequently precedes matching and de- duplication because entity-resolution algorithms perform more effectively when predictable representational differences have already been normalized.
Reference Data and Metadata Management are important supporting disciplines. Reference Data can define approved abbreviations or formats, while metadata documents transformation rules and intended meaning.
Standardization does not prove that the resulting address is factually accurate. It improves consistency and comparability. An address can be perfectly standardized yet belong to the wrong customer.
Therefore, standardization should be combined with validation, verification, matching, and source- improvement controls where appropriate.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Standardization; Cleansing; Matching; Consistency; Reference Data Management.
NEW QUESTION # 65
Emergency contact phone number would be found in which master data management program?
Answer: A
Explanation:
An emergency contact phone number belongs to the Employee master-data domain. Employee Master Data describes relatively stable information about employees that needs to be consistently identified and reused across organizational processes and systems. Contact details associated with an employee-including emergency-contact information-therefore fall naturally within an Employee MDM program rather than Product or Asset MDM.
The broader DAMA Master Data framework identifies employees as one of the recurring enterprise entities that may require coordinated management alongside customers, products, suppliers, locations, and similar core subjects. Published versions of this specific DAMA certification item likewise identify Employee as the correct domain.
Managing this information centrally or through coordinated master-data processes helps prevent conflicting phone numbers, obsolete emergency contacts, duplicate employee identities, and inconsistent values across Human Resources and other authorized systems.
The data is also sensitive personal information. Governance should therefore define ownership, access rights, retention rules, and permitted usage. Quality controls should address completeness, validity, currency, and accuracy, because outdated emergency-contact information may fail precisely when it is needed.
Metadata Management should record definitions and classifications so that "emergency contact phone number" is consistently distinguished from an employee's own mobile or work telephone number.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Master Data Domains; Employee Master Data; Data Governance; Chapter 13 - Accuracy, Completeness and Currency.
NEW QUESTION # 66
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